def __call__(self, examples):
batch_size = len(examples)
mels = [example[0] for example in examples]
wavs = [example[1] for example in examples]
mels = batch_spec(mels, pad_value=self.padding_value)
wavs = batch_wav(wavs, pad_value=self.padding_value)
audio_starts = np.zeros((batch_size, ), dtype=np.int64)
return mels, wavs, audio_starts
def __call__(self, samples):
# transform them first
if self.valid:
samples = [(audio, mel_spectrogram, 0)
for audio, mel_spectrogram in samples]
else:
samples = [self.random_crop(sample) for sample in samples]
# batch them
audios = [sample[0] for sample in samples]
audio_starts = [sample[2] for sample in samples]
mels = [sample[1] for sample in samples]
mels = batch_spec(mels)
if self.valid:
audios = batch_wav(audios, dtype=np.float32)
else:
audios = np.array(audios, dtype=np.float32)
audio_starts = np.array(audio_starts, dtype=np.int64)
return audios, mels, audio_starts
def __call__(self, examples):
mels = [example[0] for example in examples]
wavs = [example[1] for example in examples]
mels = batch_spec(mels, pad_value=self.padding_value)
wavs = batch_wav(wavs, pad_value=self.padding_value)
return mels, wavs